A large volume of produced water (PW) has been produced as a result of extensive industrialization and rising energy demands. PW comprises organic and inorganic pollutants, such as oil, heavy metals, aliphatic hydrocarbons, and radioactive materials. The increase in PW volume globally may result in irreversible environmental damage due to the pollutants' complex nature. Several conventional treatment methods, including physical, chemical, and biological methods, are available for produced water treatment that can reduce the environmental damages. Studies have shown that adsorption is a useful technique for PW treatment and may be more effective than conventional techniques. However, the application of adsorption when treating PW is not well recorded. In the current review, the removal efficiencies of adsorbents in PW treatment are critically analyzed. An overview is provided on the merits and demerits of the adsorption techniques, focusing on overall water composition, regulatory discharge limits, and the hazardous effects of the pollutants. Moreover, this review highlights a potential alternative to conventional technologies, namely, porous adsorbent materials known as metal-organic frameworks (MOFs), demonstrating their significance and efficiency in removing contaminants. This study suggests ways to overcome the existing limitations of conventional adsorbents, which include low surface area and issues with reuse and regeneration. Moreover, it is concluded that there is a need to develop highly porous, efficient, eco-friendly, cost-effective, mechanically stable, and sustainable MOF hybrids for produced water treatment.
Mixed-matrix membranes (MMMs) have been reported to have considerable scope in gas separation applications because of their merged inherent strength of a durable polymer matrix and the exceptional performance capabilities of inorganic fillers. The selection of comparatively suitable polymers with fillers that can match each other and boost interfacial compatibility while ensuring uniform dispersion of filler within the polymer is still intensively demanding and is challenging at the experimental scale. Ionic liquids (ILs) are effective in promoting better dispersion and compatibility, leading to improved separation performance. A computational molecular simulation approach is employed in current work to design a hybrid membrane having Trioctapropyl phosphonium bis(trifluoromethylsulfonyl)imide [P8883][Tf2N] IL decorated silica as a filler and 4,4'-(hexafluoroisopropylidene)diphthalic anhydride-4,4'-oxydianiline (6FDA-ODA) polymer for carbon dioxide (CO2) separation from methane (CH4). Thermophysical and gas transport properties under pure and mixed gas condition (30, 50, and 70% CO2/CH4) within the MMMs with varying filler loadings (5, 10, and 15 wt% IL-silica) are examined via Grand Canonical Monte Carlo (GCMC) and Molecular Dynamics (MD) simulations. Membrane characteristics like glass transition temperature (T g), Fractional Free Volume (v f), X-Ray Diffraction (XRD), solubility, diffusivity, permeability, and selectivity for neat and IL-silica filled 6FDA-ODA are computed. The results show that the T g of the composite membrane with 5 wt% IL-silica is found to be considerably higher (with 305 °C) than that of the pure 6FDA-ODA polymer having 298 °C. A higher T g value highlights the effective dispersion and higher adhesion between the filler and polymer membrane. Additionally, CO2 permeability for 5 wt% IL-silica/6FDA-ODA MMM is significantly improved, measuring 319.0 barrer while maintaining a CO2/CH4 selectivity of 16.2. These values are 89% and 56% respectively, greater than the corresponding values of neat 6FDA-ODA membrane. Published data from the literature review is used to validate the findings and guarantee their reliability. The obtained results exhibited an error in the range of 0.7-9%. Hence, it is concluded from the study that molecular simulation can be used to design IL decorated silica incorporated within 6FDA-ODA matrix, which is able to boost the interfacial compatibility, with elevated CO2/CH4 selectivity and CO2 permeability.
The experimental determination of thermophysical properties of nanofluid (NF) is time-consuming and costly, leading to the use of soft computing methods such as response surface methodology (RSM) and artificial neural network (ANN) to estimate these properties. The present study involves modelling and optimization of thermal conductivity and viscosity of NF, which comprises multi-walled carbon nanotubes (MWCNTs) and thermal oil. The modelling is performed to predict the thermal conductivity and viscosity of NF by using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Both models were tested and validated, which showed promising results. In addition, a detailed optimization study was conducted to investigate the optimum thermal conductivity and viscosity by varying temperature and NF weight per cent. Four case studies were explored using different objective functions based on NF application in various industries. The first case study aimed to maximize thermal conductivity (0.15985 W/m oC) while minimizing viscosity (0.03501 Pa s) obtained at 57.86 °C and 0.85 NF wt%. The goal of the second case study was to minimize thermal conductivity (0.13949 W/m °C) and viscosity (0.02526 Pa s) obtained at 55.88 °C and 0.15 NF wt%. The third case study targeted maximizing thermal conductivity (0.15797 W/m °C) and viscosity (0.07611 Pa s), and the optimum temperature and NF wt% were 30.64 °C and 0.0.85,' respectively. The last case study explored the minimum thermal conductivity (0.13735) and maximum viscosity (0.05263 Pa s) obtained at 30.64 °C and 0.15 NF wt%.